The matplotlib module produces high quality plots. With it you can turn your
data or your models into figures for presentations or articles. No need to do
the numerical work in one program, save the data, and plot it with another
program.

Using IPython makes interactive work easy. Data processing, exploration of
numerical models, trying out operations on-the-fly allows to go quickly from
an idea to a result. See the IPython site for many
examples.

There is a sizeable collection of both generic and
application-specific numerical and scientific code, written using
Python, NumPy and SciPy. Don’t reinvent the wheel, there may already
be a pre-made solution for your problem. See
Topical Software for a partial list.

As Python is a popular general-purpose programming language, it has
many advanced modules for building for example interactive
applications (see e.g. wxPython and Traits) or web sites (see
e.g. Django). Using SciPy with these is a quick way to build a
fully-fledged scientific application.

Python is a programming language, and there are several ways to
approach it. There is no single program that you can start and that
gives an integrated user experience. Instead, there are several
possible ways to work with Python.

The most common is to use the advanced interactive Python shell
IPython to enter commands and run scripts. Scripts can be written
with any text editor, for instance Emacs, Vim or even Notepad. Some
of the packages such as Python(x,y) mentioned in Installing the SciPy Stack also
offer an integrated scientific development environment.

Neither SciPy nor NumPy provide plotting functions. There are several
plotting packages available for Python, the most commonly used one being
matplotlib.

To give a simple example of typical interactive use, we find and plot
the maximum of a Bessel function. If you have worked with numerical
computation environments before, what follows looks very familiar.